Academic literature on the topic 'Complexity'
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Journal articles on the topic "Complexity"
Chow, Robert. "Complexity of Complexin." Biophysical Journal 106, no. 2 (January 2014): 11a. http://dx.doi.org/10.1016/j.bpj.2013.11.110.
Full textBLOWS, M. W. "Complexity for complexity's sake?" Journal of Evolutionary Biology 20, no. 1 (January 2007): 39–44. http://dx.doi.org/10.1111/j.1420-9101.2006.01241.x.
Full textShoemaker, Jessica. "Complexity's Shadow: American Indian Property, Sovereignty, and the Future." Michigan Law Review, no. 115.4 (2017): 487. http://dx.doi.org/10.36644/mlr.115.4.complexity.
Full textPöltner, P., and T. Grechenig. "Organic Finance Framework: Aligning Financing Complexity with Organisational Complexity (for Innovative Companies)." International Journal of Trade, Economics and Finance 11, no. 6 (December 2020): 156–62. http://dx.doi.org/10.18178/ijtef.2020.11.6.682.
Full textKeune, Hans. "Critical complexity in environmental health practice: simplify and complexify." Environmental Health 11, Suppl 1 (2012): S19. http://dx.doi.org/10.1186/1476-069x-11-s1-s19.
Full textRead, Dwight, and Claes Andersson. "Cultural complexity and complexity evolution." Adaptive Behavior 28, no. 5 (January 20, 2019): 329–58. http://dx.doi.org/10.1177/1059712318822298.
Full textGoldreich, Oded, Rafail Ostrovsky, and Erez Petrank. "Computational Complexity and Knowledge Complexity." SIAM Journal on Computing 27, no. 4 (August 1998): 1116–41. http://dx.doi.org/10.1137/s0097539795280524.
Full textLINIAL, NATI, and ADI SHRAIBMAN. "Learning Complexity vs Communication Complexity." Combinatorics, Probability and Computing 18, no. 1-2 (March 2009): 227–45. http://dx.doi.org/10.1017/s0963548308009656.
Full textLachish, Oded, Ilan Newman, and Asaf Shapira. "Space Complexity Vs. Query Complexity." computational complexity 17, no. 1 (April 2008): 70–93. http://dx.doi.org/10.1007/s00037-008-0239-z.
Full textIván Tarride, Mario. "The complexity of measuring complexity." Kybernetes 42, no. 2 (February 2013): 174–84. http://dx.doi.org/10.1108/03684921311310558.
Full textDissertations / Theses on the topic "Complexity"
Baumler, Raphaël. "La sécurité de marché et son modèle maritime : entre dynamiques du risque et complexité des parades : les difficultés pour construire la sécurité." Thesis, Evry-Val d'Essonne, 2009. http://www.theses.fr/2009EVRY0024/document.
Full textModels of development, capitalism and industrialism are also big dynamics of risk by their ability altering social world. At the level of firms, innovation and competition requires ongoing adjustment. Subject to their owners, companies focus on financial risk. Other risks are subordinate to the primary target. The dynamics of risk are changing the firm at the rate of external demands. The competition justifies harmful cost reductions and destabilizing re-engineering. The aim of safety is to reduce the uprising conditions of risk. Safety is a complex social building. Locally, safety seems a melt of man and tools within an organization. Overall, control of the safety is a challenge between risk and cost in the unit. Between cost and efficiency, management makes its own choice. As the shipowner and his vessel, the factory management has the keys to safety. It arbitrates between budgets and plays competition between territories. Ensuring impunity, equivalence and non-discrimination, international law guarantees competition between all States and flags. With globalization, we entered the era of the safety market. Safety is one of the production factors in global competition. Business leaders incorporate it into their overall strategies. With this factor in mind they choose their factories geographical location but also the allocation of budgets inside the firm. In selecting safety participants, the Executive create a unique picture of what safety is that corresponds to their paradigms. The rebuilding of safety in production units is played locally but also globally and by discovering the complexities of the dynamics of risk and the way of building safety
Rubiano, Thomas. "Implicit Computational Complexity and Compilers." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCD076/document.
Full textLa théorie de la complexité´e s’intéresse à la gestion des ressources, temps ou espace, consommés par un programmel ors de son exécution. L’analyse statique nous permet de rechercher certains critères syntaxiques afin de catégoriser des familles de programmes. L’une des approches les plus fructueuses dans le domaine consiste à observer le comportement potentiel des données manipulées. Par exemple, la détection de programmes “non size increasing” se base sur le principe très simple de compter le nombre d’allocations et de dé-allocations de mémoire, en particulier au cours de boucles et on arrive ainsi à détecter les programmes calculant en espace constant. Cette méthode s’exprime très bien comme propriété sur les graphes de flot de contrôle. Comme les méthodes de complexité implicite fonctionnent à l’aide de critères purement syntaxiques, ces analyses peuvent être faites au moment de la compilation. Parce qu’elles ne sont ici que statiques, ces analyses ne sont pas toujours calculables ou facilement calculables, des compromis doivent être faits en s’autorisant des approximations. Dans le sillon du “Size-Change Principle” de C. S. Lee, N. D. Jones et A. M. Ben-Amram, beaucoup de recherches reprennent cette méthode de prédiction de terminaison par observation de l’évolution des ressources. Pour le moment, ces méthodes venant des théories de la complexité implicite ont surtout été appliquées sur des langages plus ou moins jouets. Cette thèse tend à porter ces méthodes sur de “vrais” langages de programmation en s’appliquant au niveau des représentations intermédiaires dans des compilateurs largement utilises. Elle fournit à la communauté un outil permettant de traiter une grande quantité d’exemples et d’avoir une idée plus précise de l’expressivité réelle de ces analyses. De plus cette thèse crée un pont entre deux communautés, celle de la complexité implicite et celle de la compilation, montrant ainsi que chacune peut apporter à l’autre
Pankratov, Denis. "Communication complexity and information complexity." Thesis, The University of Chicago, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3711791.
Full textInformation complexity enables the use of information-theoretic tools in communication complexity theory. Prior to the results presented in this thesis, information complexity was mainly used for proving lower bounds and direct-sum theorems in the setting of communication complexity. We present three results that demonstrate new connections between information complexity and communication complexity.
In the first contribution we thoroughly study the information complexity of the smallest nontrivial two-party function: the AND function. While computing the communication complexity of AND is trivial, computing its exact information complexity presents a major technical challenge. In overcoming this challenge, we reveal that information complexity gives rise to rich geometrical structures. Our analysis of information complexity relies on new analytic techniques and new characterizations of communication protocols. We also uncover a connection of information complexity to the theory of elliptic partial differential equations. Once we compute the exact information complexity of AND, we can compute exact communication complexity of several related functions on n-bit inputs with some additional technical work. Previous combinatorial and algebraic techniques could only prove bounds of the form Θ( n). Interestingly, this level of precision is typical in the area of information theory, so our result demonstrates that this meta-property of precise bounds carries over to information complexity and in certain cases even to communication complexity. Our result does not only strengthen the lower bound on communication complexity of disjointness by making it more exact, but it also shows that information complexity provides the exact upper bound on communication complexity. In fact, this result is more general and applies to a whole class of communication problems.
In the second contribution, we use self-reduction methods to prove strong lower bounds on the information complexity of two of the most studied functions in the communication complexity literature: Gap Hamming Distance (GHD) and Inner Product mod 2 (IP). In our first result we affirm the conjecture that the information complexity of GHD is linear even under the uniform distribution. This strengthens the Ω(n) bound shown by Kerenidis et al. (2012) and answers an open problem by Chakrabarti et al. (2012). We also prove that the information complexity of IP is arbitrarily close to the trivial upper bound n as the permitted error tends to zero, again strengthening the Ω(n) lower bound proved by Braverman and Weinstein (2011). More importantly, our proofs demonstrate that self-reducibility makes the connection between information complexity and communication complexity lower bounds a two-way connection. Whereas numerous results in the past used information complexity techniques to derive new communication complexity lower bounds, we explore a generic way, in which communication complexity lower bounds imply information complexity lower bounds in a black-box manner.
In the third contribution we consider the roles that private and public randomness play in the definition of information complexity. In communication complexity, private randomness can be trivially simulated by public randomness. Moreover, the communication cost of simulating public randomness with private randomness is well understood due to Newman's theorem (1991). In information complexity, the roles of public and private randomness are reversed: public randomness can be trivially simulated by private randomness. However, the information cost of simulating private randomness with public randomness is not understood. We show that protocols that use only public randomness admit a rather strong compression. In particular, efficient simulation of private randomness by public randomness would imply a version of a direct sum theorem in the setting of communication complexity. This establishes a yet another connection between the two areas. (Abstract shortened by UMI.)
Smith, Peter. "Adaptive leadership: fighting complexity with complexity." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/42728.
Full textContemporary crises have become increasingly complex and the methods of leading through them have failed to keep pace. If it is assumed that leadership matters—that it has a legitimate effect on the outcome of a crisis, then leaders have a duty to respond to that adaptation with modifications of their own. Using literature sources, the research explores crisis complexity, crisis leadership, and alternative leadership strategies. Specifically, the research evaluates the applicability of complexity science to current crises. Having identified the manner in which crises have changed, it focuses on the gap between contemporary crises and the current methods of crisis leadership. The paper pursues adaptive methods of leading in complex crises and examines a number of alternative strategies for addressing the gap. The research suggests that a combination of recognizing the complexity of contemporary crises, applying resourceful solutions, and continually reflecting on opportunities to innovate, may be an effective way to lead through complex crises using complex leadership.
Chen, Lijie S. M. Massachusetts Institute of Technology. "Fine-grained complexity meets communication complexity." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122754.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 215-229).
Fine-grained complexity aims to understand the exact exponent of the running time of fundamental problems in P. Basing on several important conjectures such as Strong Exponential Time Hypothesis (SETH), All-Pair Shortest Path Conjecture, and the 3-Sum Conjecture, tight conditional lower bounds are proved for numerous exact problems from all fields of computer science, showing that many text-book algorithms are in fact optimal. For many natural problems, a fast approximation algorithm would be as important as fast exact algorithms. So it would be interesting to show hardness for approximation algorithms as well. But we had few techniques to prove tight hardness for approximation problems in P--In particular, the celebrated PCP Theorem, which proves similar approximation hardness in the world of NP-completeness, is not fine-grained enough to yield interesting conditional lower bounds for approximation problems in P.
In 2017, a breakthrough work of Abboud, Rubinstein and Williams [12] established a framework called "Distributed PCP", and applied that to show conditional hardness (under SETH) for several fundamental approximation problems in P. The most interesting aspect of their work is a connection between fine-grained complexity and communication complexity, which shows Merlin-Arther communication protocols can be utilized to give fine-grained reductions between exact and approximation problems. In this thesis, we further explore the connection between fine-grained complexity and communication complexity. More specifically, we have two sets of results. In the first set of results, we consider communication protocols other than Merlin-Arther protocols in [12] and show that they can be used to construct other fine-grained reductions between problems. [sigma]₂ Protocols and An Equivalence Class for Orthogonal Vectors (OV).
First, we observe that efficient [sigma]₂[superscripts cc] protocols for a function imply fine-grained reductions from a certain related problem to OV. Together with other techniques including locality-sensitive hashing, we establish an equivalence class for OV with O(log n) dimensions, including Max-IP/Min-IP, approximate Max-IP/Min-IP, and approximate bichromatic closest/further pair. · NP · UPP Protocols and Hardness for Computational Geometry Problems in 2⁰([superscript log*n]) Dimensions. Second, we consider NP · UPP protocols which are the relaxation of Merlin-Arther protocols such that Alice and Bob only need to be convinced with probability > 1/2 instead of > 2/3.
We observe that NP · UPP protocols are closely connected to Z-Max-IP problem in very small dimensions, and show that Z-Max-IP, l₂₋-Furthest Pair and Bichromatic l₂-Closest Pair in 2⁰[superscript (log* n)] dimensions requires n²⁻⁰[superscript (1)] time under SETH, by constructing an efficient NP - UPP protocol for the Set-Disjointness problem. This improves on the previous hardness result for these problems in w(log² log n) dimensions by Williams [172]. · IP Protocols and Hardness for Approximation Problems Under Stronger Conjectures. Third, building on the connection between IP[superscript cc] protocols and a certain alternating product problem observed by Abboud and Rubinstein [11] and the classical IP = PSPACE theorem [123, 155]. We show that several finegrained problems are hard under conjectures much stronger than SETH (e.g., the satisfiability of n⁰[superscript (1)]-depth circuits requires 2(¹⁻⁰[superscript (1)n] time).
In the second set of results, we utilize communication protocols to construct new algorithms. · BQP[superscript cc] Protocols and Approximate Counting Algorithms. Our first connection is that a fast BQP[superscript cc] protocol for a function f implies a fast deterministic additive approximate counting algorithm for a related pair counting problem. Applying known BQP[superscript cc] protocols, we get fast deterministic additive approximate counting algorithms for Count-OV (#OV), Sparse Count-OV and Formula of SYM circuits. · AM[superscript cc]/PH[superscript cc] Protocols and Efficient SAT Algorithms. Our second connection is that a fast AM[superscript cc] (or PH[superscript cc]) protocol for a function f implies a faster-than-bruteforce algorithm for a related problem.
In particular, we show that if the Longest Common Subsequence (LCS) problem admits a fast (computationally efficient) PH[superscript cc] protocol (polylog(n) complexity), then polynomial-size Formula-SAT admits a 2[superscript n-n][superscript 1-[delta]] time algorithm for any constant [delta] > 0, which is conjectured to be unlikely by a recent work of Abboud and Bringmann [6].
by Lijie Chen.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Gopalakrishnan, K. S. "Complexity cores in average-case complexity theory." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1473222.
Full textBrochenin, Rémi. "Separation logic : expressiveness, complexity, temporal extension." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00956587.
Full textOtto, James R. (James Ritchie). "Complexity doctrines." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29104.
Full textAda, Anil. "Communication complexity." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=121119.
Full textLa complexité de communication étudie combien de bits un groupe de joueurs donné doivent échanger entre eux pour calculer une function dont l'input est distribué parmi les joueurs. Bien que ce soit un domaine de recherche naturel basé sur des considérations pratiques, la motivation principale vient des nombreuses applications théoriques.Cette thèse comporte trois parties principales, étudiant trois aspects de la complexité de communication.1. La première partie discute le modèle 'number on the forehead' (NOF) dans la complexité de communication à plusieurs joueurs. Il s'agit d'un modèle fondamental en complexité de communication, avec des applications à la complexité des circuits, la complexité des preuves, les programmes de branchement et la théorie de Ramsey. Dans ce modèle, nous étudions les fonctions composeés f de g. Ces fonctions comprennent la plupart des fonctions bien connues qui sont étudiées dans la littérature de la complexité de communication. Un objectif majeur est de comprendre quelles combinaisons de f et g produisent des compositions qui sont difficiles du point de vue de la communication. En particulier, à cause de l'importance des applications aux circuits, il est intéressant de comprendre la puissance du modèle NOF quand le nombre de joueurs atteint ou dépasse log n. Motivé par ces objectifs nous montrons l'existence d'un protocole simultané efficace à k joueurs de coût O(log^3 n) pour sym de g lorsque k > 1 + log n, sym est une function symmétrique quelconque et g est une fonction arbitraire. Nous donnons aussi des applications de notre protocole efficace à la théorie de Ramsey.Dans le contexte où k < log n, nous étudions de plus près des fonctions de la forme majority de g, mod_m de g et nor de g, où les deux derniers cas sont des généralisations des fonctions bien connues et très étudiées Inner Product et Disjointness respectivement. Nous caractérisons la complexité de communication de ces fonctions par rapport au choix de g.2. La deuxième partie considère les applications de l'analyse de Fourier des fonctions symmétriques à la complexité de communication et autres domaines. La norme spectrale d'une function booléenne f:{0,1}^n -> {0,1} est la somme des valeurs absolues de ses coefficients de Fourier. Nous donnons une caractérisation combinatoire pour la norme spectrale des fonctions symmétriques. Nous montrons que le logarithme de la norme spectrale est du même ordre de grandeur que r(f)log(n/r(f)), avec r(f) = max(r_0,r_1) où r_0 et r_1 sont les entiers minimaux plus petits que n/2 pour lesquels f(x) ou f(x)parity(x) est constant pour tout x tel que x_1 + ... + x_n à [r_0,n-r_1]. Nous présentons quelques applications aux arbres de décision et à la complexité de communication des fonctions symmétriques.3. La troisième partie étudie la confidentialité dans le contexte de la complexité de communication: quelle quantité d'information est-ce que les joueurs révèlent sur leur input en suivant un protocole donné? L'inatteignabilité de la confidentialité parfaite pour plusieurs fonctions motivent l'étude de la confidentialité approximative. Feigenbaum et al. (Proceedings of the 11th Conference on Electronic Commerce, 167--178, 2010) ont défini des notions de confidentialité approximative dans le pire cas et dans le cas moyen, et ont présenté plusieurs bornes supérieures intéressantes ainsi que quelques questions ouvertes. Dans cette thèse, nous obtenons des bornes asymptotiques précises, pour le pire cas aussi bien que pour le cas moyen, sur l'échange entre la confidentialité approximative de protocoles et le coût de communication pour les enchères Vickrey Auction, qui constituent l'exemple canonique d'une enchère honnête. Nous démontrons aussi des bornes inférieures exponentielles sur la confidentialité approximative de protocoles calculant la function Intersection, indépendamment du coût de communication. Ceci résout une conjecture de Feigenbaum et al.
Mariotti, Humberto, and Cristina Zauhy. "Managing Complexity." Universidad Peruana de Ciencias Aplicadas (UPC), 2014.
Find full textBooks on the topic "Complexity"
Watanabe, Osamu, ed. Kolmogorov Complexity and Computational Complexity. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77735-6.
Full text1958-, Watanabe Osamu, ed. Kolmogorov complexity and computational complexity. Berlin: Springer-Verlag, 1992.
Find full textWatanabe, Osamu. Kolmogorov Complexity and Computational Complexity. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992.
Find full textGivón, T., and Masayoshi Shibatani, eds. Syntactic Complexity. Amsterdam: John Benjamins Publishing Company, 2009. http://dx.doi.org/10.1075/tsl.85.
Full textMiestamo, Matti, Kaius Sinnemäki, and Fred Karlsson, eds. Language Complexity. Amsterdam: John Benjamins Publishing Company, 2008. http://dx.doi.org/10.1075/slcs.94.
Full textDehmer, Matthias, Frank Emmert-Streib, and Herbert Jodlbauer, eds. Entrepreneurial Complexity. Boca Raton, FL : CRC Press, 2018.: CRC Press, 2019. http://dx.doi.org/10.1201/9781351250849.
Full textSkobelev, Petr, and George Rzevski. Managing Complexity. Warrendale, PA: SAE International, 2014. http://dx.doi.org/10.4271/1845649362.
Full textHickey, Anthony J., and Hugh D. C. Smyth. Pharmaco-Complexity. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-7856-1.
Full textGlattfelder, James B. Decoding Complexity. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33424-5.
Full textDowney, R. G., and M. R. Fellows. Parameterized Complexity. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-0515-9.
Full textBook chapters on the topic "Complexity"
Jörg, Ton. "The Complexity of Complexity." In New Thinking in Complexity for the Social Sciences and Humanities, 197–206. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1303-1_13.
Full textAllender, Eric. "The Complexity of Complexity." In Computability and Complexity, 79–94. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50062-1_6.
Full textLee, Li Way, and Aaron Keathley. "Complexity: The Complexity Fever." In 45 Conversations About Behavioral Economics, 35–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05046-6_9.
Full textKlir, George J. "Complexity." In Facets of Systems Science, 135–57. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1331-5_8.
Full textCvitkovic, Emilio. "Complexity." In Competition, 193–225. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1007/978-1-349-12857-0_8.
Full textBoy, Guy André. "Complexity." In Human–Computer Interaction Series, 87–106. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30270-6_5.
Full textForsdyke, Donald R. "Complexity." In Evolutionary Bioinformatics, 267–91. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7771-7_14.
Full textPeitgen, Heinz-Otto, Hartmut Jürgens, Dietmar Saupe, Evan Maletsky, Terry Perciante, and Lee Yunker. "Complexity." In Fractals for the Classroom: Strategic Activities Volume One, 69–108. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4613-9047-3_3.
Full textHammer, Barbara. "Complexity." In Learning with recurrent neural networks, 103–31. London: Springer London, 2000. http://dx.doi.org/10.1007/bfb0110021.
Full textSteinhart, Eric. "Complexity." In Believing in Dawkins, 23–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43052-8_2.
Full textConference papers on the topic "Complexity"
Taguchi, Chihiro, and David Chiang. "Language Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn’t." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 15493–503. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.827.
Full textFlack, Jessica. "Complexity begets complexity." In The 2021 Conference on Artificial Life. Cambridge, MA: MIT Press, 2021. http://dx.doi.org/10.1162/isal_a_00470.
Full textMORIN, EDGAR. "RESTRICTED COMPLEXITY, GENERAL COMPLEXITY." In Worldviews, Science and Us - Philosophy and Complexity. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812707420_0002.
Full text"Proceedings of Computational Complexity (Formerly Structure in Complexity Theory)." In Proceedings of Computational Complexity (Formerly Structure in Complexity Theory). IEEE, 1996. http://dx.doi.org/10.1109/ccc.1996.507662.
Full textLinial, Nati, and Adi Shraibman. "Learning Complexity vs. Communication Complexity." In 2008 23rd Annual IEEE Conference on Computational Complexity. IEEE, 2008. http://dx.doi.org/10.1109/ccc.2008.28.
Full textAgrawal, M., and V. Arvind. "A note on decision versus search for graph automorphism." In Proceedings of Computational Complexity (Formerly Structure in Complexity Theory). IEEE, 1996. http://dx.doi.org/10.1109/ccc.1996.507689.
Full text"Author index." In Proceedings of Computational Complexity (Formerly Structure in Complexity Theory). IEEE, 1996. http://dx.doi.org/10.1109/ccc.1996.507693.
Full textCerra, Daniele, and Mihai Datcu. "Algorithmic Cross-Complexity and Relative Complexity." In 2009 Data Compression Conference (DCC). IEEE, 2009. http://dx.doi.org/10.1109/dcc.2009.6.
Full textKushilevitz, Eyal, and Enav Weinreb. "On the complexity of communication complexity." In the 41st annual ACM symposium. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1536414.1536479.
Full textBabai, Laszlo, Peter Frankl, and Janos Simon. "Complexity classes in communication complexity theory." In 27th Annual Symposium on Foundations of Computer Science (sfcs 1986). IEEE, 1986. http://dx.doi.org/10.1109/sfcs.1986.15.
Full textReports on the topic "Complexity"
Ackermann, Mark R., Nancy Kay Hayden, and Wendell Jones. Complexity and Simplicity: Putting Complexity Science in Perspective. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1481586.
Full textWhite, D., M. Stowell, and K. Lange. Automatic Complexity Reduction. Office of Scientific and Technical Information (OSTI), November 2014. http://dx.doi.org/10.2172/1179112.
Full textCarvalho, Leandro, and Dan Silverman. Complexity and Sophistication. Cambridge, MA: National Bureau of Economic Research, July 2019. http://dx.doi.org/10.3386/w26036.
Full textSalant, Yuval, and Jorg Spenkuch. Complexity and Choice. Cambridge, MA: National Bureau of Economic Research, April 2022. http://dx.doi.org/10.3386/w30002.
Full textEnke, Benjamin, Thomas Graeber, and Ryan Oprea. Complexity and Time. Cambridge, MA: National Bureau of Economic Research, March 2023. http://dx.doi.org/10.3386/w31047.
Full textGomez-Gonzalez, Jose E., Jorge M. Uribe, and Oscar Valencia. Sovereign Risk and Economic Complexity. Inter-American Development Bank, January 2024. http://dx.doi.org/10.18235/0005533.
Full textBlakesley, Paul J. Operational Shock Complexity Theory. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada437516.
Full textJanusz, Paul E. The Complexity Analysis Tool. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada201700.
Full textValle Jr, Vicente. Chaos, Complexity and Deterrence. Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada432927.
Full textStreufert, Siegfried, Rosanne M. Pogash, and Mary T. Piasecki. Training for Cognitive Complexity. Fort Belvoir, VA: Defense Technical Information Center, March 1987. http://dx.doi.org/10.21236/ada181828.
Full text